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- How do you visualize data with many categories?
- What visual would you use if you have multiple categories to show over time?
- Which type of visualization is best when you want to compare proportions with multiple categories?
- Which visualization can be used to identify the time taken for different categories?
- Which visualization tool shows all individual data points?
- Which visualizations can you use to display the composition of a whole from different parts?
How do you visualize data with many categories?
To visualize a small data set containing multiple categorical (or qualitative) variables, you can create either a bar plot, a balloon plot or a mosaic plot.
What visual would you use if you have multiple categories to show over time?
Line Graph
A line graph reveals trends or progress over time and you can use it to show many different categories of data. You should use it when you chart a continuous data set.
Which type of visualization is best when you want to compare proportions with multiple categories?
Bubble charts, or bubble graphs, are among the best data visualization graphs for comparing several values or sets of data at a glance.
Which visualization can be used to identify the time taken for different categories?
Column Chart
This is one of the most common types of data visualization tools. There's a reason we learn how to make column charts in elementary school. They're a simple, time-honored way to show a comparison among different sets of data. You can also use a column chart to track data sets over time.
Which visualization tool shows all individual data points?
A histogram is similar to a bar graph but has a different plotting system. Histograms are the best data visualization type to analyze ranges of data according to a specific frequency.
Which visualizations can you use to display the composition of a whole from different parts?
The pie chart is the most popular way to visualize composition, thanks to its simple design. The ingredients or elements are presented as fractional parts of a whole.
